人工智能生成的答案草案集成到健康记录和医生的电子通信中
Ming Tai-Seale1,2, Sally L Baxter2,3, Florin Vaida4
1Department of Family Medicine, University of California San Diego School of Medicine, La Jolla.
JAMA network open
|April 15, 2024
概括
在患者消息中使用生成人工智能 (GenAI) 增加了医生阅读时间和回复时间,但没有改变回复时间. 需要进一步的研究来评估GenAI.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床工作流的优化 临床工作流优化
背景情况:
- 医生的工作负担是一个重大问题,患者的信息有助于增加行政负担.
- 生成型人工智能 (GenAI) 为简化沟通和减少医生倦怠提供了潜在的解决方案.
- 基因人工智能对医生的时间和沟通效率的影响需要经验研究.
研究的目的:
- 调查GenAI为患者信息起草的回复与医生在回复信息上花费的时间之间的联系.
- 评估GenAI对医生回复患者信息的长度的影响.
- 评估医生对GenAI工具在临床沟通中的实用性的看法.
主要方法:
- 在一个学术卫生系统中进行了一项随机等候名单质量改善 (QI) 研究.
- 初级保健医生被随机分为立即和延迟的GenAI激活组.
- 混合效应模型分析了消息阅读时间,回复时间和回复长度的变化.
主要成果:
- 使用GenAI与阅读消息时间增加21.8%有关 (P=.008).
- 没有观察到医生响应时间的显著变化 (P=.33).
- 基因人工智能起草的回复显著增加了信息长度17.9% (P<.001).
结论:
- 患者消息中GenAI起草的回复导致阅读时间和回复长度增加,但没有改变回复时间.
- 医生认为GenAI工具具有价值,但也确定了需要改进的领域.
- 进一步的严格研究对于验证GenAI的性能和探索其对患者体验和不同AI模型的影响至关重要.
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